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  1. #!/bin/bash
  2.  
  3. NUM_CLASSES=$1
  4.  
  5. echo "
  6. [net]
  7. # Testing
  8. #batch=1
  9. #subdivisions=1
  10. # Training
  11. batch=16
  12. subdivisions=1
  13. width=416
  14. height=416
  15. channels=3
  16. momentum=0.9
  17. decay=0.0005
  18. angle=0
  19. saturation = 1.5
  20. exposure = 1.5
  21. hue=.1
  22.  
  23. learning_rate=0.001
  24. burn_in=1000
  25. max_batches = 500200
  26. policy=steps
  27. steps=400000,450000
  28. scales=.1,.1
  29.  
  30. [convolutional]
  31. batch_normalize=1
  32. filters=32
  33. size=3
  34. stride=1
  35. pad=1
  36. activation=leaky
  37.  
  38. # Downsample
  39.  
  40. [convolutional]
  41. batch_normalize=1
  42. filters=64
  43. size=3
  44. stride=2
  45. pad=1
  46. activation=leaky
  47.  
  48. [convolutional]
  49. batch_normalize=1
  50. filters=32
  51. size=1
  52. stride=1
  53. pad=1
  54. activation=leaky
  55.  
  56. [convolutional]
  57. batch_normalize=1
  58. filters=64
  59. size=3
  60. stride=1
  61. pad=1
  62. activation=leaky
  63.  
  64. [shortcut]
  65. from=-3
  66. activation=linear
  67.  
  68. # Downsample
  69.  
  70. [convolutional]
  71. batch_normalize=1
  72. filters=128
  73. size=3
  74. stride=2
  75. pad=1
  76. activation=leaky
  77.  
  78. [convolutional]
  79. batch_normalize=1
  80. filters=64
  81. size=1
  82. stride=1
  83. pad=1
  84. activation=leaky
  85.  
  86. [convolutional]
  87. batch_normalize=1
  88. filters=128
  89. size=3
  90. stride=1
  91. pad=1
  92. activation=leaky
  93.  
  94. [shortcut]
  95. from=-3
  96. activation=linear
  97.  
  98. [convolutional]
  99. batch_normalize=1
  100. filters=64
  101. size=1
  102. stride=1
  103. pad=1
  104. activation=leaky
  105.  
  106. [convolutional]
  107. batch_normalize=1
  108. filters=128
  109. size=3
  110. stride=1
  111. pad=1
  112. activation=leaky
  113.  
  114. [shortcut]
  115. from=-3
  116. activation=linear
  117.  
  118. # Downsample
  119.  
  120. [convolutional]
  121. batch_normalize=1
  122. filters=256
  123. size=3
  124. stride=2
  125. pad=1
  126. activation=leaky
  127.  
  128. [convolutional]
  129. batch_normalize=1
  130. filters=128
  131. size=1
  132. stride=1
  133. pad=1
  134. activation=leaky
  135.  
  136. [convolutional]
  137. batch_normalize=1
  138. filters=256
  139. size=3
  140. stride=1
  141. pad=1
  142. activation=leaky
  143.  
  144. [shortcut]
  145. from=-3
  146. activation=linear
  147.  
  148. [convolutional]
  149. batch_normalize=1
  150. filters=128
  151. size=1
  152. stride=1
  153. pad=1
  154. activation=leaky
  155.  
  156. [convolutional]
  157. batch_normalize=1
  158. filters=256
  159. size=3
  160. stride=1
  161. pad=1
  162. activation=leaky
  163.  
  164. [shortcut]
  165. from=-3
  166. activation=linear
  167.  
  168. [convolutional]
  169. batch_normalize=1
  170. filters=128
  171. size=1
  172. stride=1
  173. pad=1
  174. activation=leaky
  175.  
  176. [convolutional]
  177. batch_normalize=1
  178. filters=256
  179. size=3
  180. stride=1
  181. pad=1
  182. activation=leaky
  183.  
  184. [shortcut]
  185. from=-3
  186. activation=linear
  187.  
  188. [convolutional]
  189. batch_normalize=1
  190. filters=128
  191. size=1
  192. stride=1
  193. pad=1
  194. activation=leaky
  195.  
  196. [convolutional]
  197. batch_normalize=1
  198. filters=256
  199. size=3
  200. stride=1
  201. pad=1
  202. activation=leaky
  203.  
  204. [shortcut]
  205. from=-3
  206. activation=linear
  207.  
  208.  
  209. [convolutional]
  210. batch_normalize=1
  211. filters=128
  212. size=1
  213. stride=1
  214. pad=1
  215. activation=leaky
  216.  
  217. [convolutional]
  218. batch_normalize=1
  219. filters=256
  220. size=3
  221. stride=1
  222. pad=1
  223. activation=leaky
  224.  
  225. [shortcut]
  226. from=-3
  227. activation=linear
  228.  
  229. [convolutional]
  230. batch_normalize=1
  231. filters=128
  232. size=1
  233. stride=1
  234. pad=1
  235. activation=leaky
  236.  
  237. [convolutional]
  238. batch_normalize=1
  239. filters=256
  240. size=3
  241. stride=1
  242. pad=1
  243. activation=leaky
  244.  
  245. [shortcut]
  246. from=-3
  247. activation=linear
  248.  
  249. [convolutional]
  250. batch_normalize=1
  251. filters=128
  252. size=1
  253. stride=1
  254. pad=1
  255. activation=leaky
  256.  
  257. [convolutional]
  258. batch_normalize=1
  259. filters=256
  260. size=3
  261. stride=1
  262. pad=1
  263. activation=leaky
  264.  
  265. [shortcut]
  266. from=-3
  267. activation=linear
  268.  
  269. [convolutional]
  270. batch_normalize=1
  271. filters=128
  272. size=1
  273. stride=1
  274. pad=1
  275. activation=leaky
  276.  
  277. [convolutional]
  278. batch_normalize=1
  279. filters=256
  280. size=3
  281. stride=1
  282. pad=1
  283. activation=leaky
  284.  
  285. [shortcut]
  286. from=-3
  287. activation=linear
  288.  
  289. # Downsample
  290.  
  291. [convolutional]
  292. batch_normalize=1
  293. filters=512
  294. size=3
  295. stride=2
  296. pad=1
  297. activation=leaky
  298.  
  299. [convolutional]
  300. batch_normalize=1
  301. filters=256
  302. size=1
  303. stride=1
  304. pad=1
  305. activation=leaky
  306.  
  307. [convolutional]
  308. batch_normalize=1
  309. filters=512
  310. size=3
  311. stride=1
  312. pad=1
  313. activation=leaky
  314.  
  315. [shortcut]
  316. from=-3
  317. activation=linear
  318.  
  319.  
  320. [convolutional]
  321. batch_normalize=1
  322. filters=256
  323. size=1
  324. stride=1
  325. pad=1
  326. activation=leaky
  327.  
  328. [convolutional]
  329. batch_normalize=1
  330. filters=512
  331. size=3
  332. stride=1
  333. pad=1
  334. activation=leaky
  335.  
  336. [shortcut]
  337. from=-3
  338. activation=linear
  339.  
  340.  
  341. [convolutional]
  342. batch_normalize=1
  343. filters=256
  344. size=1
  345. stride=1
  346. pad=1
  347. activation=leaky
  348.  
  349. [convolutional]
  350. batch_normalize=1
  351. filters=512
  352. size=3
  353. stride=1
  354. pad=1
  355. activation=leaky
  356.  
  357. [shortcut]
  358. from=-3
  359. activation=linear
  360.  
  361.  
  362. [convolutional]
  363. batch_normalize=1
  364. filters=256
  365. size=1
  366. stride=1
  367. pad=1
  368. activation=leaky
  369.  
  370. [convolutional]
  371. batch_normalize=1
  372. filters=512
  373. size=3
  374. stride=1
  375. pad=1
  376. activation=leaky
  377.  
  378. [shortcut]
  379. from=-3
  380. activation=linear
  381.  
  382. [convolutional]
  383. batch_normalize=1
  384. filters=256
  385. size=1
  386. stride=1
  387. pad=1
  388. activation=leaky
  389.  
  390. [convolutional]
  391. batch_normalize=1
  392. filters=512
  393. size=3
  394. stride=1
  395. pad=1
  396. activation=leaky
  397.  
  398. [shortcut]
  399. from=-3
  400. activation=linear
  401.  
  402.  
  403. [convolutional]
  404. batch_normalize=1
  405. filters=256
  406. size=1
  407. stride=1
  408. pad=1
  409. activation=leaky
  410.  
  411. [convolutional]
  412. batch_normalize=1
  413. filters=512
  414. size=3
  415. stride=1
  416. pad=1
  417. activation=leaky
  418.  
  419. [shortcut]
  420. from=-3
  421. activation=linear
  422.  
  423.  
  424. [convolutional]
  425. batch_normalize=1
  426. filters=256
  427. size=1
  428. stride=1
  429. pad=1
  430. activation=leaky
  431.  
  432. [convolutional]
  433. batch_normalize=1
  434. filters=512
  435. size=3
  436. stride=1
  437. pad=1
  438. activation=leaky
  439.  
  440. [shortcut]
  441. from=-3
  442. activation=linear
  443.  
  444. [convolutional]
  445. batch_normalize=1
  446. filters=256
  447. size=1
  448. stride=1
  449. pad=1
  450. activation=leaky
  451.  
  452. [convolutional]
  453. batch_normalize=1
  454. filters=512
  455. size=3
  456. stride=1
  457. pad=1
  458. activation=leaky
  459.  
  460. [shortcut]
  461. from=-3
  462. activation=linear
  463.  
  464. # Downsample
  465.  
  466. [convolutional]
  467. batch_normalize=1
  468. filters=1024
  469. size=3
  470. stride=2
  471. pad=1
  472. activation=leaky
  473.  
  474. [convolutional]
  475. batch_normalize=1
  476. filters=512
  477. size=1
  478. stride=1
  479. pad=1
  480. activation=leaky
  481.  
  482. [convolutional]
  483. batch_normalize=1
  484. filters=1024
  485. size=3
  486. stride=1
  487. pad=1
  488. activation=leaky
  489.  
  490. [shortcut]
  491. from=-3
  492. activation=linear
  493.  
  494. [convolutional]
  495. batch_normalize=1
  496. filters=512
  497. size=1
  498. stride=1
  499. pad=1
  500. activation=leaky
  501.  
  502. [convolutional]
  503. batch_normalize=1
  504. filters=1024
  505. size=3
  506. stride=1
  507. pad=1
  508. activation=leaky
  509.  
  510. [shortcut]
  511. from=-3
  512. activation=linear
  513.  
  514. [convolutional]
  515. batch_normalize=1
  516. filters=512
  517. size=1
  518. stride=1
  519. pad=1
  520. activation=leaky
  521.  
  522. [convolutional]
  523. batch_normalize=1
  524. filters=1024
  525. size=3
  526. stride=1
  527. pad=1
  528. activation=leaky
  529.  
  530. [shortcut]
  531. from=-3
  532. activation=linear
  533.  
  534. [convolutional]
  535. batch_normalize=1
  536. filters=512
  537. size=1
  538. stride=1
  539. pad=1
  540. activation=leaky
  541.  
  542. [convolutional]
  543. batch_normalize=1
  544. filters=1024
  545. size=3
  546. stride=1
  547. pad=1
  548. activation=leaky
  549.  
  550. [shortcut]
  551. from=-3
  552. activation=linear
  553.  
  554. ######################
  555.  
  556. [convolutional]
  557. batch_normalize=1
  558. filters=512
  559. size=1
  560. stride=1
  561. pad=1
  562. activation=leaky
  563.  
  564. [convolutional]
  565. batch_normalize=1
  566. size=3
  567. stride=1
  568. pad=1
  569. filters=1024
  570. activation=leaky
  571.  
  572. [convolutional]
  573. batch_normalize=1
  574. filters=512
  575. size=1
  576. stride=1
  577. pad=1
  578. activation=leaky
  579.  
  580. [convolutional]
  581. batch_normalize=1
  582. size=3
  583. stride=1
  584. pad=1
  585. filters=1024
  586. activation=leaky
  587.  
  588. [convolutional]
  589. batch_normalize=1
  590. filters=512
  591. size=1
  592. stride=1
  593. pad=1
  594. activation=leaky
  595.  
  596. [convolutional]
  597. batch_normalize=1
  598. size=3
  599. stride=1
  600. pad=1
  601. filters=1024
  602. activation=leaky
  603.  
  604. [convolutional]
  605. size=1
  606. stride=1
  607. pad=1
  608. filters=$(expr 3 \* $(expr $NUM_CLASSES \+ 5))
  609. activation=linear
  610.  
  611.  
  612. [yolo]
  613. mask = 6,7,8
  614. anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
  615. classes=$NUM_CLASSES
  616. num=9
  617. jitter=.3
  618. ignore_thresh = .7
  619. truth_thresh = 1
  620. random=1
  621.  
  622.  
  623. [route]
  624. layers = -4
  625.  
  626. [convolutional]
  627. batch_normalize=1
  628. filters=256
  629. size=1
  630. stride=1
  631. pad=1
  632. activation=leaky
  633.  
  634. [upsample]
  635. stride=2
  636.  
  637. [route]
  638. layers = -1, 61
  639.  
  640.  
  641.  
  642. [convolutional]
  643. batch_normalize=1
  644. filters=256
  645. size=1
  646. stride=1
  647. pad=1
  648. activation=leaky
  649.  
  650. [convolutional]
  651. batch_normalize=1
  652. size=3
  653. stride=1
  654. pad=1
  655. filters=512
  656. activation=leaky
  657.  
  658. [convolutional]
  659. batch_normalize=1
  660. filters=256
  661. size=1
  662. stride=1
  663. pad=1
  664. activation=leaky
  665.  
  666. [convolutional]
  667. batch_normalize=1
  668. size=3
  669. stride=1
  670. pad=1
  671. filters=512
  672. activation=leaky
  673.  
  674. [convolutional]
  675. batch_normalize=1
  676. filters=256
  677. size=1
  678. stride=1
  679. pad=1
  680. activation=leaky
  681.  
  682. [convolutional]
  683. batch_normalize=1
  684. size=3
  685. stride=1
  686. pad=1
  687. filters=512
  688. activation=leaky
  689.  
  690. [convolutional]
  691. size=1
  692. stride=1
  693. pad=1
  694. filters=$(expr 3 \* $(expr $NUM_CLASSES \+ 5))
  695. activation=linear
  696.  
  697.  
  698. [yolo]
  699. mask = 3,4,5
  700. anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
  701. classes=$NUM_CLASSES
  702. num=9
  703. jitter=.3
  704. ignore_thresh = .7
  705. truth_thresh = 1
  706. random=1
  707.  
  708.  
  709.  
  710. [route]
  711. layers = -4
  712.  
  713. [convolutional]
  714. batch_normalize=1
  715. filters=128
  716. size=1
  717. stride=1
  718. pad=1
  719. activation=leaky
  720.  
  721. [upsample]
  722. stride=2
  723.  
  724. [route]
  725. layers = -1, 36
  726.  
  727.  
  728.  
  729. [convolutional]
  730. batch_normalize=1
  731. filters=128
  732. size=1
  733. stride=1
  734. pad=1
  735. activation=leaky
  736.  
  737. [convolutional]
  738. batch_normalize=1
  739. size=3
  740. stride=1
  741. pad=1
  742. filters=256
  743. activation=leaky
  744.  
  745. [convolutional]
  746. batch_normalize=1
  747. filters=128
  748. size=1
  749. stride=1
  750. pad=1
  751. activation=leaky
  752.  
  753. [convolutional]
  754. batch_normalize=1
  755. size=3
  756. stride=1
  757. pad=1
  758. filters=256
  759. activation=leaky
  760.  
  761. [convolutional]
  762. batch_normalize=1
  763. filters=128
  764. size=1
  765. stride=1
  766. pad=1
  767. activation=leaky
  768.  
  769. [convolutional]
  770. batch_normalize=1
  771. size=3
  772. stride=1
  773. pad=1
  774. filters=256
  775. activation=leaky
  776.  
  777. [convolutional]
  778. size=1
  779. stride=1
  780. pad=1
  781. filters=$(expr 3 \* $(expr $NUM_CLASSES \+ 5))
  782. activation=linear
  783.  
  784.  
  785. [yolo]
  786. mask = 0,1,2
  787. anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
  788. classes=$NUM_CLASSES
  789. num=9
  790. jitter=.3
  791. ignore_thresh = .7
  792. truth_thresh = 1
  793. random=1
  794. " >> yolov3-custom.cfg
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